Multi-Sensor-Based Action Monitoring and Recognition via Hybrid Descriptors and Logistic Regression

Hafeez, Sadaf; Alotaibi, Saud S.; Alazeb, Abdulwahab; Mudawi, Naif Al; Kim, Wooseong

doi:10.1109/access.2023.3275733

Cited by 4 publications

(4 citation statements)

References 63 publications

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“…Ranieri et al proposed an indoor action recognition method for the elderly using the RGB-D camera images captured by a humanoid robot, wearable inertial sensors, and surrounding sensors attached to items inside a home [19]. Hafeez et al [20] proposed a system that effectively detects human falls and daily activities using wearable accelerometers and video data collected by cameras directly worn on the body. These studies based on heterogeneous data addressed quality issues inherent in homogeneous data and demonstrated excellent prediction performance.…”

Section: Behavior Predictionmentioning

confidence: 99%

Enhanced Pet Behavior Prediction via S2GAN-Based Heterogeneous Data Synthesis

Kim,

Moon

2024

Applied Sciences

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Heterogeneous data have been used to enhance behavior prediction performance; however, it involves issues such as missing data, which need to be addressed. This paper proposes enhanced pet behavior prediction via Sensor to Skeleton Generative Adversarial Networks (S2GAN)-based heterogeneous data synthesis. The S2GAN model synthesizes the key features of video skeletons based on collected nine-axis sensor data and replaces missing data, thereby enhancing the accuracy of behavior prediction. In this study, data collected from 10 pets in a real-life-like environment were used to conduct recognition experiments on 9 commonly occurring types of indoor behavior. Experimental results confirmed that the proposed S2GAN-based synthesis method effectively resolves possible missing data issues in real environments and significantly improves the performance of the pet behavior prediction model. Additionally, by utilizing data collected under conditions similar to the real environment, the method enables more accurate and reliable behavior prediction. This research demonstrates the importance and utility of synthesizing heterogeneous data in behavior prediction, laying the groundwork for applications in various fields such as abnormal behavior detection and monitoring.

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Section: Behavior Predictionmentioning

confidence: 99%

Enhanced Pet Behavior Prediction via S2GAN-Based Heterogeneous Data Synthesis

Kim,

Moon

2024

Applied Sciences

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“…The results were classified using a Support Vector Machine (SVM) and K-nearest Neighbour (KNN), where optimal performance was achieved when all features from the RGB-D camera, accelerometer, and gyroscope were used for classification. Previous research recognized Activities of Daily Living (ADL) and falls using RGB, depth, and accelerometer sensors [36], where 3 public datasets were evaluated with Logistic Regression (LR) algorithms. The proposed model achieved the accuracy of 0.93 and features extracted from both time and frequency domains included skewness, fuzzy entropy, temporal moment, and geometric (skeleton).…”

Section: Related Workmentioning

confidence: 99%

“…The use of multiple sensors such as accelerometer, gyroscope, magnetometer, heart rate, and camera has also been investigated [3,33,35,36,40]. The results showed that the use of multiple sensors could enhance the performance of machine learning classifiers.…”

Section: Related Workmentioning

confidence: 99%

“…The effectiveness of using a single sensor to reduce data dimensions still requires investigation. Regarding features usage, investigations have been carried out focusing on time and frequency domains, producing entropy, energy, and statistical features, including mean, median, standard deviation, average, minimum, maximum, skewness, and other statistical values [3,33,35,36,38,39]. Moreover, the combination of these mentioned features with others required exploration.…”

Section: Related Workmentioning

confidence: 99%

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A New Approach for Human Activity Recognition (HAR) Using A Single Tri-axial Accelerometer Based on a Combination of Three Feature Subsets

2024

IJIES

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Human Activity Recognition (HAR) is focused on Activities of Daily Living and developed in the health and human security fields. The HAR concept was introduced in previous research using multi-sensor devices. In their implementation, wearable devices require computational and real-time environmental limitations. This paper proposed a new approach for HAR using a machine learning-based single-sensor accelerometer. This research aimed to determine the performance of machine learning in HAR using three Feature Subsets: Feature Subset Signal Vector Magnitude (SMA), Feature Subset Fast Fourier Transform (FFT), and Feature Subset Value-Crossing. In features selection, ANOVA was used to reduce feature dimensionality. The experimental results have been assessed using the confusion matrix to prove that the proposed model can achieve an optimal accuracy of 0.97, higher than several state-of-the-art approaches. The optimal sensitivity and specificity values have been 0.98 and 0.99 and are partially higher than previous studies using similar testing scenarios.

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